We first tested the re-weighting scheme proposed in [1]}. The ResNet18 architecture was trained using a weighted loss, where the weights were chosen so that the disparate impact was equal to 1 (reweighted strategy).
We also loaded the pre-trained ResNet18 architecture of Torchvisionhttps://pytorch.org/vision/stable/index.html and trained its last layer on our EuroSAT data to use the generic transformed image representation of this pre-trained network. It is important to mention that a very large and generic ImageNet database was used for pre-training (Pre-trained, No fine tuning strategy). We alternatively fine-tuned all layers of this pre-trained network to simultaneously optimize the transformed image representation and the prediction based on this representation, i.e. the parts 1 and 2 of the neural-network in Fig. REF  (Pre-trained, Fine tuning strategy). It is important to note that we only trained for 5 epochs instead of 50 when fine-tuning the pre-trained neural-networks in order to avoid overfitting.
Finally, we randomly drew the initial state of the neural network and trained all layers, but thoroughly distinguished the convergence for all images and for the group of blue-veiled images only. In this case, we stopped training the ResNet18 parameters when an over-fitting phenomenon started being observed in the blue-veiled images (Convergence aware strategy). Results are shown in Fig. REF -(b). A typical detailed convergence of the Convergence aware strategy is also shown in Fig. REF .
